Abstract

High quality conversions of scanned documents into PDF
usually either rely on full OCR or token compression. This
paper describes an approach intermediate between those
two: it is based on token clustering, but additionally groups
tokens into candidate fonts. Our approach has the potential of yielding OCR-like PDFs when the inputs are high
quality and degrading to token based compression when the
font analysis fails, while preserving full visual fidelity. Our
approach is based on an unsupervised algorithm for grouping tokens into candidate fonts. The algorithm constructs a
graph based on token proximity and derives token groups by
partitioning this graph. In initial experiments on scanned
300 dpi pages containing multiple fonts, this technique reconstructs candidate fonts with 100% accuracy.